Electroencephalography (EEG) is an important toolfor the study of the human brain because it provides potentiallyuseful signals for understanding the spatial and temporal dynam-ics of neural information processing. These signals are commonlyrepresented by vector or matrix mathematical structures, whichmay counteract their natural behaviour for a multidimensionalrepresentation. Thus, in this case, the information from an EEGsignal should be represented using tensors. This study presentsan analysis of how these different mathematical structures canbe explored to obtain functional brain information. Two matrixmodels and one tensor model were investigated and assessed usingbrain maps and classification results. Our results show at leastthree different and complementary ways for the representationof cognitive brain maps and, as far as our exploratory analysis isconcerned, the tensorial model stands out in terms of the highestlevel of compression and precision in comparison to the othermodels.

中文翻译：

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脑电信号表示的线性模型和张量模型的比较


脑电图（EEG）是研究人脑的重要工具，因为它为理解神经信息处理的空间和时间动态提供了潜在有用的信号。这些信号通常由向量或矩阵数学结构表示，这可能会抵消它们的多维表示的自然行为。因此，在这种情况下，来自脑电图信号的信息应该使用张量来表示。这项研究分析了如何探索这些不同的数学结构来获取功能性大脑信息。使用脑图和分类结果对两种矩阵模型和一种张量模型进行了研究和评估。我们的结果显示了至少三种不同且互补的方式来表示认知脑图，并且就我们的探索性分析而言，与其他模型相比，张量模型在最高水平的压缩和精度方面脱颖而出。